>From: "Charles Goodwin" <[EMAIL PROTECTED]> >Hi, I have just joined this list after seeing it mentioned on the Fabric of >Reality list....

Hi. BTW, what's up on the FOR list? Ever see anything interesting there? I thought the book sucked except for chapter 2 (I think; the one explaining the MWI), but at least there are some MWIers on that list I would think. >Would someone mind briefly explaining what FIN is (or at least what the >letters stand for)? Is it some version of QTI (Quantum >theory of immortality) ? Yes, any version of "QTI" is FIN. >Why should a typical observer find himself to be older than the apparent >lifetime of his species? I guess you mean "assuming FIN, why ..." >so *very* few observeres are going to notice the TU versions of anyone >else. So the only way to actually experience this phenomenon is to live to >be that old yourself. Right ... >I must ask, though, what makes you think that a typical observer ISN'T much >older than the lifetime of his species would allow? I'm not so old, but if FIN were true, the effective chance of me being old would be 100%. So by Bayesian reasoning, it must be false. >Given that you can't observe anyone but yourself in this state (or it's >"TU" that you ever will) (and I'm assuming you haven't reached 120 yet), >you can't really use a self-sampling argument on this, surely? On the contrary, you do use a SSA. After all, you will never (for any question) have more than the one data point for use in the SSA. But with a probability of 0% or 100%, that's plenty! > > It means - and I admit it does take a little thought here - _I want >to follow a guessing procedure that, in general, maximizes the fraction of >those people (who use that procedure) who get the right guess_. (Why would >I want a more error-prone method?) So I use Bayesian reasoning with the >best prior available, the uniform one on observer-moments, which maximizes >the fraction of observer-moments who guess right. No soul-hopping in that >reasoning, I assure you. > >I'm sorry, I still don't see how that applies to me. If I know which >observer moments I'm in (e.g. I know how old I am) why should I >reason as though I don't? Because you want to know things, don't you? It's no different from any Bayesian reasoning, in that regard. Suppose you know that you just flipped a coin 10 times in a row, and it landed on heads all ten times. Now you can apply Bayesian reasoning to guess whether it is a 2-headed coin, or a regular coin. How to do it? p(2-headed|got 10 heads) = [p(got 10 heads|2-headed) p_0(2-headed)] / N p(1-headed|got 10 heads) = [p(got 10 heads|1-headed) p_0(1-headed)] / N where N = p(got 10 heads) is the normalization factor so that these two conditional probabilities sum to 1 (they are the only possibilities). That's a standard use of Bayes' theorem. But - whoa there - what's the p(got 10 heads) and the like? You already _know_ you got 10 heads, so why not just set p(got 10 heads) to 1? Obviously, you consider the counterfactual case of (didn't get 10 heads) for a reason - that is, to help you guess something about the coin. In the same way, the SSA helps you guess things. It's just a procedure to follow which usually helps the people that use it to make correct guesses. - - - - - - - Jacques Mallah ([EMAIL PROTECTED]) Physicist / Many Worlder / Devil's Advocate "I know what no one else knows" - 'Runaway Train', Soul Asylum My URL: http://hammer.prohosting.com/~mathmind/ _________________________________________________________________ Get your FREE download of MSN Explorer at http://explorer.msn.com/intl.asp